Search for additional heavy neutral Higgs and gauge bosons in the ditau final state produced in 36 fb−1 of pp collisions at √s=13 TeV with the ATLAS detector

A search for heavy neutral Higgs bosons and Z′ bosons is performed using a data sample corresponding to an integrated luminosity of 36.1 fb−1 from proton-proton collisions at √s=13 TeV recorded by the ATLAS detector at the LHC during 2015 and 2016. The heavy resonance is assumed to decay to τ+τ− with at least one tau lepton decaying to final states with hadrons and a neutrino. The search is performed in the mass range of 0.2-2.25 TeV for Higgs bosons and 0.2-4.0 TeV for Z′ bosons. The data are in good agreement with the background predicted by the Standard Model. The results are interpreted in benchmark scenarios. In the context of the hMSSM scenario, the data exclude tan β > 1.0 for mA= 0.25 TeV and tan β > 42 for mA=1.5 TeV at the 95% confidence level. For the Sequential Standard Model, ZSSM′ with mZ′< 2.42 TeV is excluded at 95% confidence level, while Z NU′ with mZ ′ < 2.25 TeV is excluded for the non-universal G(221) model that exhibits enhanced couplings to third-generation fermions

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

Nano-Crystalline &Amorphous Silicon PhotoTransistor Performance Analysis

In this thesis, we compared electrical performance and stability of a novel nanocrystalline Si (nc-Si) thin film phototransistor (TFT) phototransistor and a regular amorphous silicon (a-Si:H) TFT phototransistor for large area imaging applications. The electrical performance parameters of nc-Si TFT phototransistor were extracted from the electrical (current-voltage) testing in dark and under illumination. The field-effect mobility is found to be around 1.2 cm2V-1s-1, the threshold voltage around 3.9V and the sub-threshold voltage slope around 0.47V/Dec. Optical properties of nc-Si TFT phototransistor have been evaluated under the green light illumination in the range of 1014 – 1017 lum, and the photocurrent gain and the external quantum efficiency were extracted from the experimental results. By comparing the results with those for a-Si:H TFTs measured under the same conditions, we found that nc-Si TFT has higher photo current gain under low illumination intensity, 5 ×1014 to 7 ×1015 lum. This thesis shows the relations bewteen the photo current gain, the external quantum efficiency, TFT drain and TFT gate bias; the photo current gain and the external quantum efficiency can be controlled by the Vds and the Vgs